Fusing Priori and Posteriori Metrics for Automatic Dataset Annotation of Planar Grasping

Hao Sha, Lai Qianen, Hongxiang Yu, Rong Xiong, Yue Wang
Proceedings of The 6th Conference on Robot Learning, PMLR 205:1050-1059, 2023.

Abstract

Grasp detection based on deep learning has been a research hot spot in recent years. The performance of grasping detection models relies on high-quality, large-scale grasp datasets. Taking comprehensive consideration of quality, extendability, and annotation cost, metric-based simulation methodology is the most promising way to generate grasp annotation. As experts in grasping, human intuitively tends to make grasp decision based both on priori and posteriori knowledge. Inspired by that, a combination of priori and posteriori grasp metrics is intuitively helpful to improve annotation quality. In this paper, we build a hybrid metric group involving both priori and posteriori metrics and propose a grasp evaluator to merge those metrics to approximate human grasp decision capability. Centered on the evaluator, we have constructed an automatic grasp annotation framework, through which a large-scale, high-quality, low annotation cost planar grasp dataset GMD is automatically generated.

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-sha23a, title = {Fusing Priori and Posteriori Metrics for Automatic Dataset Annotation of Planar Grasping}, author = {Sha, Hao and Qianen, Lai and Yu, Hongxiang and Xiong, Rong and Wang, Yue}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {1050--1059}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/sha23a/sha23a.pdf}, url = {https://proceedings.mlr.press/v205/sha23a.html}, abstract = {Grasp detection based on deep learning has been a research hot spot in recent years. The performance of grasping detection models relies on high-quality, large-scale grasp datasets. Taking comprehensive consideration of quality, extendability, and annotation cost, metric-based simulation methodology is the most promising way to generate grasp annotation. As experts in grasping, human intuitively tends to make grasp decision based both on priori and posteriori knowledge. Inspired by that, a combination of priori and posteriori grasp metrics is intuitively helpful to improve annotation quality. In this paper, we build a hybrid metric group involving both priori and posteriori metrics and propose a grasp evaluator to merge those metrics to approximate human grasp decision capability. Centered on the evaluator, we have constructed an automatic grasp annotation framework, through which a large-scale, high-quality, low annotation cost planar grasp dataset GMD is automatically generated.} }
Endnote
%0 Conference Paper %T Fusing Priori and Posteriori Metrics for Automatic Dataset Annotation of Planar Grasping %A Hao Sha %A Lai Qianen %A Hongxiang Yu %A Rong Xiong %A Yue Wang %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-sha23a %I PMLR %P 1050--1059 %U https://proceedings.mlr.press/v205/sha23a.html %V 205 %X Grasp detection based on deep learning has been a research hot spot in recent years. The performance of grasping detection models relies on high-quality, large-scale grasp datasets. Taking comprehensive consideration of quality, extendability, and annotation cost, metric-based simulation methodology is the most promising way to generate grasp annotation. As experts in grasping, human intuitively tends to make grasp decision based both on priori and posteriori knowledge. Inspired by that, a combination of priori and posteriori grasp metrics is intuitively helpful to improve annotation quality. In this paper, we build a hybrid metric group involving both priori and posteriori metrics and propose a grasp evaluator to merge those metrics to approximate human grasp decision capability. Centered on the evaluator, we have constructed an automatic grasp annotation framework, through which a large-scale, high-quality, low annotation cost planar grasp dataset GMD is automatically generated.
APA
Sha, H., Qianen, L., Yu, H., Xiong, R. & Wang, Y.. (2023). Fusing Priori and Posteriori Metrics for Automatic Dataset Annotation of Planar Grasping. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:1050-1059 Available from https://proceedings.mlr.press/v205/sha23a.html.

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